Papers by Tuo Ji
SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check (2021.emnlp-main)
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| Challenge: | Chinese Spelling Check is a nontrivial task because of the nature of ideographic language. |
| Approach: | They propose a pretrained model with graph-based extra features that captures erroneous patterns . they use a graph neural network to introduce radical and pinyin information as visual and phonetic features. |
| Outcome: | The proposed model can show competitive performance on OCR datasets where most errors are not covered by existing confusion set. |
RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining (2022.acl-long)
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| Challenge: | Large-scale pretrained language models have achieved SOTA results on NLP tasks but are vulnerable to adversarial attacks especially for logographic languages like Chinese. |
| Approach: | They propose a pretrained Chinese Bert that is robust to various forms of adversarial attacks like word perturbation, synonyms, typos, etc. |
| Outcome: | The proposed model outperforms baselines on 5 Chinese NLU tasks without sacrificing performance on clean testsets. |
A Unified Generative Framework for Aspect-based Sentiment Analysis (2021.acl-long)
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| Challenge: | Existing complicated ABSA models focus on subtasks, which leads to complicated solutions . et al., j. c. d. r., and j dr. s. v. present a unified approach to solve seven subtask tasks in one framework. |
| Approach: | They redefine every subtask target as a sequence mixed by pointer indexes and sentiment class indexe . they exploit the pre-training sequence-to-sequence model BART to solve all ABSA subtasks in an end-to end framework. |
| Outcome: | The proposed framework achieves substantial performance gain and provides a real unified solution for the whole ABSA subtasks. |